Navigating Share Buyback Programs : A Genetic Algorithm Approach to Outperform the Buyback Benchmark

In this era of rapid digital revolution within the financial sector, our paper offers a cutting-edge approach to the intricacies of share buyback programs by employing Genetic Algorithms (GAs). Navigating the complexity of financial market operations, we leverage artificial intelligence to craft a trading strategy optimized for performance in share repurchase transactions.Our exploration begins with a mathematical formulation of optimal execution, encompassing key aspects such as optimization objectives, constraints, and benchmarks. We then innovate by applying GAs, detailing their essential components within our model.Our strategy undergoes rigorous validation, demonstrating both consistent and robust performance that not only outperforms established benchmarks but suggests an average outperformance of approximately 190 basis points in real-world applications. This remarkable result translates into potential multi-billion dollar gains in a market valued at 280 billion USD.We conclude by shedding light on the transformative implications of our research. We reveal the promise of employing advanced technologies like GAs in smart trading strategies, contributing to the innovation in asset pricing and the broader FinTech dialogue. Our findings offer a compelling look into the efficiency gains possible through algorithmic trading, thereby enriching the financial industry's technological landscape.In essence, our paper stands at the forefront of FinTech innovation, pioneering the use of Genetic Algorithms in share buyback execution. It serves as a testament to the exciting potential of modern computational techniques, perfectly aligned with the ongoing digital revolution and innovation in the finance sector